How do smart cameras recognise transparent and reflective objects? SingTown Technology's OpenMV solves competition challenges.

In robot competitions, vehicles or robotic arms often need to identify the position of target objects against complex backgrounds to accomplish path planning or pick-and-place tasks.

This project requires the identification of targets, which are semi-transparent acrylic hemispherical covers, approximately 10 centimetres in diameter, with a 2-centimetre border at the base. They are placed centrally on the indoor floor within a 4×4 metre area. There can be up to three targets, and their positions are fixed.

The background is a non-solid colour floor with simple patterns, which is prone to reflections and lighting interference.

To achieve precise coordinate recognition and pose estimation, the team plans to utilise SingTown's OpenMV intelligent camera as the on-board vision module for real-time detection and positioning of the acrylic cover.

The system must output the central point information of the target in the image coordinate system and transmit the data to the small vehicle's main control system to achieve automatic navigation, path adjustment, or task triggering.

The robotics competition project utilises a transparent target detection system built upon SingTown Technology's OpenMV intelligent camera. Its computer vision and image recognition technology, by integrating reflective feature detection, edge extraction, and morphological analysis algorithms, enables precise identification of semi-transparent acrylic covers under complex lighting conditions.

Due to the strong reflectivity and weak edge contours of acrylic material, the system first employs dynamic thresholding and background adaptive algorithms to suppress interference from ground patterns. It then uses Sobel edge detection and morphological closing operations to extract the edge contours of transparent acrylic. To address reflections, the system utilises brightness distribution and highlight detection, effectively distinguishing real boundaries from specular reflections.

Upon completion of identification, the program automatically calculates the centroid coordinates (x, y) of the acrylic cover within the camera's field of view and outputs them to the main control board of the trolley via the serial port.

When detecting multiple targets, the algorithm can filter based on target area or position, identifying the hood within the central area of the site as the primary target.

Practical testing indicates that this solution achieves a recognition success rate exceeding 93% in indoor environments with uniform lighting, with a positional error of less than 1.5 centimetres (with the camera positioned 30 centimetres above the ground).

SingTown's OpenMV embedded vision system operates at a high speed (frame rate above 20fps), making it suitable for scenarios requiring edge deployment such as robotics competitions, vehicle navigation, and robotic arm grasping.

Its advantages include not requiring markers, having strong adaptability to transparent objects, and being able to stably output coordinate signals under different ground patterns, providing precise visual input for automatic control systems.